A product recognition system comprises a product image data collector arranged to capture image data which is representative of an image of a product item. The product recognition system also comprises an acoustic energy source arranged to emit acoustic energy towards the product item. The product recognition further comprises a product acoustic data collector arranged to (i) capture acoustic energy deflected from the product item, (ii) process the captured acoustic data which has been deflected from the product item to provide product acoustic data which is representative of one or more characteristics of the product item, and (iii) compare the product acoustic data with a store of reference acoustic data to provide one or more subsets of items against which the captured image data can be compared to identify the product item.
|
1. A product recognition system comprising:
a product image data collector arranged to capture image data which is representative of an image of a product item;
an acoustic energy source arranged to emit acoustic energy towards the product item; and
a product acoustic data collector arranged to (i) capture acoustic energy deflected from the product item, (ii) process the captured acoustic data which has been deflected from the product item to provide product acoustic data which is representative of one or more characteristics of the product item, and (iii) compare the product acoustic data with a store of reference acoustic data to provide one or more subsets of items against which the captured image data can be compared to identify the product item.
13. A method of reducing recognition times in an image recognition system to identify a product item, the method comprising:
emitting acoustic energy towards the product item to be identified;
capturing acoustic energy deflected from the product item;
processing the captured acoustic energy to provide acoustic data which is representative of one or more characteristics of the product item;
comparing the acoustic data which is representative of one or more characteristics of the product item with a store of reference acoustic data which is representative of characteristics of a relatively large database of product items to provide a relatively small one or more subsets of the relatively large database of product items; and
providing the relatively small one or more subsets of items to the image recognition system to allow the image recognition system to process the one or more subsets of items to identify the product item.
9. An apparatus for reducing recognition times in an image-based product recognition system, the apparatus comprising:
at least one acoustic energy source arranged to emit acoustic energy towards the product item;
a store of reference acoustic data which is representative of a relatively large database of product items;
an acoustic energy capture device arranged to capture acoustic data which is representative of deflected acoustic energy from the product item; and
control circuitry arranged to (i) compare the captured acoustic data with the store of reference acoustic data, (ii) identify a relatively small subset of items from the relatively large database of items based upon the comparison of the captured acoustic data with the store of reference product acoustic data, and (iii) provide the relatively small subset of product items to the image-based product recognition system to allow the product recognition system to identify the product item based upon the relatively small subset of the relatively large database of items and thereby to assist the image-based product recognition system in identifying the product item.
2. A product recognition system according to
3. A product recognition system according to
4. A product recognition system according to
5. A product recognition system according to
6. A product recognition system according to
7. A product recognition system according to
8. A product recognition system according to
10. An apparatus according to
11. An apparatus according to
12. An apparatus according to
14. A method according to
15. A method according to
16. A method according to
17. A method according to
18. A method according to
|
The present application relates to product recognition systems, and is particularly directed to a method and apparatus for reducing recognition times in an image-based product recognition system. The method and apparatus may be embodied in an image-based product recognition system in a retail checkout environment, wherein the “product” may be either a general merchandise item, or more specifically a “produce” item (examples being fruits, vegetables, and items sold in “bulk”).
Automated or operator-assisted identification methods for identifying produce items are known. Some produce identification methods are based on image recognition. A typical image-based produce identification method based on image recognition may include an imaging camera which is used to capture produce image data associated with a produce item placed on a produce weighing scale. The captured produce image data is then processed to either identify the produce item or to display a list and/or stored images of produce items on the list for selection by a customer or an operator.
A drawback in using captured produce image data to identify a produce item is that the recognition time can be long. This would occur especially when there is a relatively large database of items against which the captured produce image data needs to be compared to identify the produce item. It would be desirable to reduce long recognition times associated with produce identification methods which are based on image recognition.
In accordance with one embodiment, a product recognition system comprises a product image data collector arranged to capture image data which is representative of an image of a product item. The product recognition system also comprises an acoustic energy source arranged to emit acoustic energy towards the product item. The product recognition further comprises a product acoustic data collector arranged to (i) capture acoustic energy deflected from the product item, (ii) process the captured acoustic data which has been deflected from the product item to provide product acoustic data which is representative of one or more characteristics of the product item, and (iii) compare the product acoustic data with a store of reference acoustic data to provide one or more subsets of items against which the captured image data can be compared to identify the product item. In this manner, the list of potential “matches” can be reduced to just a few items or, in some cases, to a single item. By reducing the number of possible candidates, any subsequent image recognition time is also reduced.
Referring to
Barcode data collector 12 reads barcode 22 on merchandise item 32 to obtain an item identification number, also known as a price look-up (PLU) number, associated with item 32. Barcode data collector 12 may be any barcode data collector, including an optical barcode scanner which uses laser beams to read barcodes. Barcode data collector 12 may be located within a checkout counter, mounted on top of a checkout counter, or be a wired or wireless portable or hand-held barcode scanner.
Scale 16 determines a weight for produce item 18. Scale 16 works in connection with barcode data collector 12, produce image data collector 14, and produce acoustic data collector 40, but may be designed to operate and be mounted separately. Weight information from scale 16 may be used to help identify produce item 18.
Produce image data collector 14 collects image data (which may comprise monochrome, color, infrared, or other image data) associated with produce item 18 for the purpose of identifying produce item 18. Produce image data collector 14 may include an image capture device (not shown) in the form of a charge coupled device (CCD). As another example, image capture device may be in the form of a complementary metal oxide semiconductor (CMOS) camera. Other types of image capture devices are also possible. Reference produce image data is collected and stored within produce data file 30. During a transaction, produce image data is collected and compared to produce image data within produce data file 30. Produce image data collector 14 may act as a primary means of identifying produce item 18. Produce image data collector 14 may be combined with barcode data collector 12 into an integrated unit.
Acoustic energy source 36 transmits acoustic energy towards produce item 18. Acoustic energy source 36 may comprise an ultrasonic or subsonic emitter, for example. Acoustic energy directed towards produce item 18 is deflected or reflected. Produce acoustic data collector 40 captures acoustic energy deflected or reflected from produce item 18. Produce acoustic data collector 40 processes the deflected or reflected acoustic energy to provide information which can be used to assist produce image data collector 14 in identifying produce item 18. Produce acoustic data collector 40 may be combined with produce image data collector 14, barcode data collector 12, and/or weigh scale 16 into an integrated unit.
Referring to
Produce image data collector 14 is mounted outside the paths of light within optical barcode scanner 12 to avoid interference with the operation of optical barcode scanner 12. In this first mounting arrangement, produce image data collector 14 is mounted above aperture 88 of optical barcode scanner 12 so that aperture 94 of produce image data collector 14 faces diagonally downward toward aperture 86 of optical barcode scanner 12. A glass piece 87 is disposed in aperture 86 and provides, in addition to its primary purpose for allowing barcode scanner 12 to identify objects, a surface on which produce item 18 can be placed.
Produce acoustic data collector 40 is mounted adjacent to one side of produce image data collector 14, and acoustic energy source 36 is mounted adjacent to an opposite side of produce image data collector 14. Acoustic energy source 36 and produce acoustic data collector 40 are disposed on the same side of produce item 18 to be identified. In this first mounting arrangement, produce acoustic data collector 40 is mounted relative to acoustic energy source 36 so that acoustic energy from emitter 38 of acoustic energy source 36 is directed towards a produce item which is placed on the top surface of weighing plate 82 of weigh scale 16 (
Although produce image data collector 14 is shown in
A second mounting arrangement is to attach housing 96 of produce image data collector 14, housing 41 of produce acoustic data collector 40, and housing 37 of acoustic energy source 36 to a pole 90 mounted to checkout counter 92. Aperture 94 of produce image data collector 14 faces downward, acoustic sensor 42 of produce acoustic data collector 40 faces downward, and emitter 38 of acoustic energy source 36 also faces downward. In this second mounting arrangement, produce acoustic data collector 40 is mounted relative to acoustic energy source 36 so that acoustic energy from emitter 38 is directed towards a produce item which is placed on the top surface of weighing plate 82 of weigh scale 16 (
Transaction terminal 20 communicates with item checkout device 10, which in turn controls operation of produce image data collector 14 to identify produce item 18. This series of operations thereby allows an operator to complete a transaction with a customer. Alternatively, transaction server 24 may identify produce item in a network of transaction terminals 20. In either case, transaction server 24 obtains item identification information from produce data file 30 and sends corresponding unit price from PLU data file 28 to transaction terminal 20. PLU data file 28 and produce data file 30 are stored within storage medium 26, but either may also be located instead at transaction terminal 20.
More specifically, when produce item 18 is placed on scale 16, weight information for produce item 18 is sent from scale 16 to transaction terminal 20 so that transaction terminal 20 can determine a price for produce item 18 based upon the weight information. When produce item 18 is on scale 16, produce item 18 is in the field of view of aperture 94 of produce image data collector 14. Transaction terminal 20 executes known produce recognition software 21 which obtains image data containing produce image information from produce image data collector 14, and compares the produce image data with reference produce image data in produce data file 30 to identify produce item 18.
After produce item 18 is identified, transaction terminal 20 obtains a unit price from PLU data file 28 for the identified produce item. Transaction terminal 20 then calculates a price for produce item 18 by multiplying the unit price by the weight of produce item obtained from scale 16. Transaction terminal 20 incorporates produce item 18 and its calculated price into a transaction record.
Referring to
The type of acoustic energy capture device 50 depends upon the type of acoustic energy source 36 used. For example, if acoustic energy source 36 emits a beam of ultrasonic energy, then acoustic energy capture device 50 receives and captures a deflected or reflected beam of ultrasound. Theoretically, the ultrasonic beam causes produce item 18 to deflect acoustic energy, vibrate, shift the frequency of the reflected or deflected acoustic energy, or simply absorb acoustic energy. Various produce items 18 may exhibit a combination of these reactions, such as reflecting a portion of the acoustic energy while converting the remaining energy into heat or vibrations.
Other types of acoustic energy capture devices are possible. As an example, acoustic energy capture device 50 may be of the type which receives and captures a deflected or reflected beam of non-ultrasonic energy. A combination of acoustic energy types may also be used by incorporating multiple types of acoustic energy emitters and/or acoustic energy collectors. Each of the acoustic data capture program 62, the acoustic data identify program 70, and the store 72 of reference acoustic data may be stored in separate storage memories or together in a single storage memory.
Control circuitry 60 controls acoustic data capture device 50 in accordance with executable instructions of acoustic data capture program 62 to capture an acoustic profile which is representative of one or more characteristics of produce item 18. Control circuitry 60 may include any number of electronic processors or microcomputers, and memory as needed for operation of produce acoustic data collector 40. Suitable electronic processors, microcomputers, and memories are known and commercially available and, therefore, will not be described.
When produce item 18 is placed on scale 16, control circuitry 60 controls acoustic energy source 36 to emit acoustic energy towards produce item 18. Acoustic data capture device 50 captures deflected energy or reflected energy from produce item 18. Control circuitry 60 controls timing of acoustic energy source 36 as to when to emit energy and when to stop emitting energy. Control circuitry 60 also controls timing of acoustic data capture device 50 as to when to capture deflected/reflected energy from produce item 18. Control circuitry 60 controls timing of acoustic energy source 36 and timing of acoustic data capture device 50 such that acoustic energy source 36 stops emitting energy when acoustic data capture device 50 starts capturing deflected/reflected energy. This allows produce acoustic data collector 40 to differentiate between energy emitted from acoustic energy source 36 and energy deflected/reflected from produce item 18.
Alternatively, acoustic energy capture device 50 may capture an acoustic profile of produce item 18 in response to operated-initiated commands from transaction terminal 20. In either case, control circuitry 60 processes the captured acoustic data which is representative of one or more characteristics of produce item 18 in accordance with executable instructions of acoustic data identify program 70 to provide a relatively small subset of items from the relatively large database of items contained in the store 72, as will be described in more detail hereinbelow.
Referring to
In step 102, a determination is made as to whether a produce item has been placed on scale 16. If determination in step 102 is negative (i.e., no produce item has been placed on scale 16), the process proceeds back to step 102 to await placement of a produce item on scale 16. However, if determination in step 102 is affirmative (i.e., a produce item has been placed on scale 16), then the process proceeds to step 104 in which acoustic energy source 36 is actuated to turn on. The process then proceeds to step 106.
In step 106, acoustic energy capture device 50 (
In step 110, acoustic data which is representative of the captured acoustic energy from produce item 18 is compared with acoustic data from the store 72 of reference acoustic data (
As shown in step 116, the categorized items are provided to produce image data collector 14 to assist produce image data collector 14 in identifying produce item 18. More specifically, control circuitry 60 sends the one or more categories of possible items to produce image data collector 14. Transaction terminal 20 then controls produce image data collector 14 using known produce recognition software 21 (
Although the above description describes a method and apparatus for assisting an image-based recognition system to identify produce items, the method and apparatus are equally useful for assisting an image-based recognition system to identify non-produce items.
Also, although the above description describes acoustic energy source 36 and produce acoustic data collector 40 being part of item checkout device 10 (
Further, although the above description describes produce acoustic data being used in conjunction with produce image data to identify a produce item, it is conceivable that produce acoustic data may be used in conjunction with a combination of other types of produce characteristics data such as produce shape data, produce area density data, and produce texture data to aid in identification of the produce item.
It should be apparent that the above description describes a method to assist produce recognition software 21 in identifying produce item 18 by providing produce recognition software 21 with a relatively small subset of items from a relatively large database of items against which produce item 18 can be compared and identified based upon the comparisons. Produce item 18 is effectively categorized into a broad product class or package type. Recognition times of produce recognition software layer 21 are reduced by categorizing produce item 18 into a broad product class or package type.
It should further be apparent that the above description describes a controlled method to assist produce recognition software 21 in identifying produce item 18 which has been placed on weighing plate 82 of scale 16. The controlled assist method is automatic in that the method is performed without human intervention.
It should also be apparent that “rigidity” or “softness” of an item is determined based upon amount or frequency, or both, of the deflected energy or reflected energy from the item. If acoustic energy source 36 and produce acoustic data collector 40 comprise a combination of acoustic types (e.g., ultrasonic, “sonic”, and subsonic), then the acoustic profile of an item may include a combination of frequency types. Rigid, semi-rigid, and soft items will each have a more unique “signature” if multiple frequencies are examined. Reaction to acoustic energy emitted from acoustic energy source 36 is different for different types of merchandise and packaging (for example, glass, plastic, cardboard, or paper). As an example, paper products usually have a relatively high dissipative effect and high energy absorption. As another example, a semi-rigid item may absorb certain frequencies but reflect other frequencies.
It should also be apparent that a product filtering method is provided in that 100,000 unique product items, for example, could be reduced to perhaps a few dozen or a few hundred product items. Since a typical large store can stock up to 100,000 unique items, but paper products may comprise only about 2000 product items, for example, product recognition software could theoretically perform fifty times faster. As another example, tens of thousands of canned goods, jars, and boxed items may be excluded when the product to be identified is determined to be a “soft” item. In a typical retail store, there may be only perhaps a few hundred “soft” items. Accordingly, product recognition times are improved when the database search is limited to the relatively small one or more subsets of product items obtained from the relatively large database of product items.
While the present invention has been illustrated by the description of example processes and system components, and while the various processes and components have been described in detail, applicant does not intend to restrict or in any way limit the scope of the appended claims to such detail. Additional modifications will also readily appear to those skilled in the art. The invention in its broadest aspects is therefore not limited to the specific details, implementations, or illustrative examples shown and described. Accordingly, departures may be made from such details without departing from the spirit or scope of applicant's general inventive concept.
Patent | Priority | Assignee | Title |
10474858, | Aug 24 2012 | Digimarc Corporation | Methods of identifying barcoded items by evaluating multiple identification hypotheses, based on data from sensors including inventory sensors and ceiling-mounted cameras |
10902544, | Oct 21 2012 | Digimarc Corporation | Methods and arrangements for identifying objects |
10963657, | Aug 30 2011 | Digimarc Corporation | Methods and arrangements for identifying objects |
11281876, | Aug 30 2011 | Digimarc Corporation | Retail store with sensor-fusion enhancements |
11288472, | Aug 30 2011 | Digimarc Corporation | Cart-based shopping arrangements employing probabilistic item identification |
Patent | Priority | Assignee | Title |
5083638, | Sep 18 1990 | OPTIMAL ROBOTICS CORP | Automated point-of-sale machine |
5589209, | Apr 24 1994 | State of Israel, Ministry of Agriculture | Method for a non-destructive determination of quality parameters in fresh produce |
6332573, | Nov 10 1998 | NCR Voyix Corporation | Produce data collector and produce recognition system |
6505775, | Apr 25 2000 | NCR Voyix Corporation | Produce data collector with enhanced LVF spectrometer |
7191698, | Apr 03 2003 | Battelle Memorial Institute | System and technique for ultrasonic determination of degree of cooking |
8113427, | Dec 18 2008 | NCR Voyix Corporation | Methods and apparatus for automated product identification in point of sale applications |
8317101, | Feb 16 2009 | NCR Voyix Corporation | Produce data collecter which collects internal produce information |
8825531, | May 12 2011 | ECR Software Corporation | Automated self-checkout system |
20020123932, | |||
20090140046, | |||
20100206951, | |||
20140036630, |
Executed on | Assignor | Assignee | Conveyance | Frame | Reel | Doc |
Jul 30 2012 | HERWIG, NATHANIEL CHRISTOPHER | NCR Corporation | ASSIGNMENT OF ASSIGNORS INTEREST SEE DOCUMENT FOR DETAILS | 028687 | /0095 | |
Jul 31 2012 | NCR Corporation | (assignment on the face of the patent) | / | |||
Jan 06 2014 | NCR Corporation | JPMORGAN CHASE BANK, N A , AS ADMINISTRATIVE AGENT | SECURITY AGREEMENT | 032034 | /0010 | |
Jan 06 2014 | NCR INTERNATIONAL, INC | JPMORGAN CHASE BANK, N A , AS ADMINISTRATIVE AGENT | SECURITY AGREEMENT | 032034 | /0010 | |
Mar 31 2016 | NCR Corporation | JPMORGAN CHASE BANK, N A | SECURITY AGREEMENT | 038646 | /0001 | |
Mar 31 2016 | NCR INTERNATIONAL, INC | JPMORGAN CHASE BANK, N A | SECURITY AGREEMENT | 038646 | /0001 | |
Oct 13 2023 | NCR Corporation | NCR Voyix Corporation | CHANGE OF NAME SEE DOCUMENT FOR DETAILS | 065820 | /0704 | |
Oct 16 2023 | NCR Voyix Corporation | BANK OF AMERICA, N A , AS ADMINISTRATIVE AGENT | SECURITY INTEREST SEE DOCUMENT FOR DETAILS | 065346 | /0168 | |
Oct 16 2023 | JPMORGAN CHASE BANK, N A , AS ADMINISTRATIVE AGENT | NCR Voyix Corporation | RELEASE OF PATENT SECURITY INTEREST | 065346 | /0531 |
Date | Maintenance Fee Events |
Mar 15 2019 | M1551: Payment of Maintenance Fee, 4th Year, Large Entity. |
Mar 15 2023 | M1552: Payment of Maintenance Fee, 8th Year, Large Entity. |
Date | Maintenance Schedule |
Sep 15 2018 | 4 years fee payment window open |
Mar 15 2019 | 6 months grace period start (w surcharge) |
Sep 15 2019 | patent expiry (for year 4) |
Sep 15 2021 | 2 years to revive unintentionally abandoned end. (for year 4) |
Sep 15 2022 | 8 years fee payment window open |
Mar 15 2023 | 6 months grace period start (w surcharge) |
Sep 15 2023 | patent expiry (for year 8) |
Sep 15 2025 | 2 years to revive unintentionally abandoned end. (for year 8) |
Sep 15 2026 | 12 years fee payment window open |
Mar 15 2027 | 6 months grace period start (w surcharge) |
Sep 15 2027 | patent expiry (for year 12) |
Sep 15 2029 | 2 years to revive unintentionally abandoned end. (for year 12) |